Abstract
This study designs a trigger to determine the number of layers of a multi-layer adaptive Kalman filter and applies it to optoelectronic infrared payload system vision. This feature reduces the number of mechanically stabilized motors, equipment weight, CPU resources, and power for an electro-optical infrared payload system. The goal is to reduce the traditional use of multiple gyroscopes to perform calibration measurements on different gimbal frames by this design. In this study, mathematical modeling was carried out for the three-axis, three-frame camera stabilizer system, and the system foundation without motor and gimbal frame was established to achieve aperture-type camera mode. The exposure of the drone’s payload structure outside the aircraft can be reduced. This study provides the adaptive Kalman filter with the offset parameters of the camera image Minimum Output Sum of Squared Error and the three-axis degrees of freedom vector and angle data on the gyroscope. By using the image processing unit, the offset was corrected at each frame per second. The experimental results show that under the same hardware, failure limit and camera field of view constraints. The processing time by this method was compared to the traditional frame correction and full image stabilization methods. The results show that the proposed method can shorten 6 microseconds under the traditional method and can be used to provide lower power consumption, lower image delay, and a larger viewing angle range.
Highlights
In response to the technological advancement of drone micro-electromechanical systems, drone costs have fallen sharply
Based on the linear minimum variance criterion, this multi-sensor information fusion method has a two-layer architecture: at the first layer, a new adaptive unscented Kalman filter (UKF) scheme for the time-varying noise covariance was developed and served as a local filter to improve the adaptability together with the estimated measurement noise covariance by applying the redundant measurement noise covariance estimation, which is isolated from the state estimation; the second layer is the fusion structure to calculate the optimal matrix weights and gives the final optimal state estimations
A constant with a radix of 4 was given as an input, plus a variable of random ±1 intervals to simulate the noise, and a single-layer extended Kalman filter was performed for the experiment
Summary
In response to the technological advancement of drone micro-electromechanical systems, drone costs have fallen sharply. It can greatly increase the processing efficiency and reduce the use of the hardware gimbal frame It further reduces the weight and resource consumption of image stabilization and can further utilize one processor to complete multiple tasks in the traditional method. [1] proposed an adaptive unscented Kalman filter for target tracking with time-varying noise covariance based on multi-sensor information fusion. In this paper, applied to the electro-optical infrared payload system’s vision offset function, the multiple layer Kalman Filter characteristic [9] was used to analyze the filter inertial measurement unit and design a trigger to set up the layer number of Kalman Filter [10] This function can decrease the electro-optical infrared payload system’s number of stabilized motors, the weight of the device, central processing unit resource, and power.
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